Overview

Dataset statistics

Number of variables24
Number of observations260
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory84.5 KiB
Average record size in memory332.9 B

Variable types

Categorical3
Numeric21

Warnings

Item has a high cardinality: 260 distinct values High cardinality
Serving Size is highly correlated with SugarsHigh correlation
Calories is highly correlated with Calories from Fat and 14 other fieldsHigh correlation
Calories from Fat is highly correlated with Calories and 10 other fieldsHigh correlation
Total Fat is highly correlated with Calories and 10 other fieldsHigh correlation
Total Fat (% Daily Value) is highly correlated with Calories and 10 other fieldsHigh correlation
Saturated Fat is highly correlated with Calories and 9 other fieldsHigh correlation
Saturated Fat (% Daily Value) is highly correlated with Calories and 9 other fieldsHigh correlation
Trans Fat is highly correlated with Calories and 4 other fieldsHigh correlation
Cholesterol is highly correlated with Calories and 11 other fieldsHigh correlation
Cholesterol (% Daily Value) is highly correlated with Calories and 11 other fieldsHigh correlation
Sodium is highly correlated with Calories and 10 other fieldsHigh correlation
Sodium (% Daily Value) is highly correlated with Calories and 10 other fieldsHigh correlation
Carbohydrates is highly correlated with Calories and 2 other fieldsHigh correlation
Carbohydrates (% Daily Value) is highly correlated with Calories and 2 other fieldsHigh correlation
Dietary Fiber is highly correlated with Sodium and 4 other fieldsHigh correlation
Dietary Fiber (% Daily Value) is highly correlated with Sodium and 4 other fieldsHigh correlation
Sugars is highly correlated with Serving Size and 4 other fieldsHigh correlation
Protein is highly correlated with Calories and 12 other fieldsHigh correlation
Vitamin A (% Daily Value) is highly correlated with Calories and 2 other fieldsHigh correlation
Calcium (% Daily Value) is highly correlated with Sugars and 1 other fieldsHigh correlation
Iron (% Daily Value) is highly correlated with Calories and 12 other fieldsHigh correlation
Serving Size is highly correlated with Sugars and 1 other fieldsHigh correlation
Calories is highly correlated with Calories from Fat and 16 other fieldsHigh correlation
Calories from Fat is highly correlated with Calories and 13 other fieldsHigh correlation
Total Fat is highly correlated with Calories and 13 other fieldsHigh correlation
Total Fat (% Daily Value) is highly correlated with Calories and 13 other fieldsHigh correlation
Saturated Fat is highly correlated with Calories and 11 other fieldsHigh correlation
Saturated Fat (% Daily Value) is highly correlated with Calories and 11 other fieldsHigh correlation
Trans Fat is highly correlated with Calories and 7 other fieldsHigh correlation
Cholesterol is highly correlated with Calories and 11 other fieldsHigh correlation
Cholesterol (% Daily Value) is highly correlated with Calories and 12 other fieldsHigh correlation
Sodium is highly correlated with Calories and 12 other fieldsHigh correlation
Sodium (% Daily Value) is highly correlated with Calories and 12 other fieldsHigh correlation
Carbohydrates is highly correlated with Calories and 2 other fieldsHigh correlation
Carbohydrates (% Daily Value) is highly correlated with Calories and 2 other fieldsHigh correlation
Dietary Fiber is highly correlated with Calories and 8 other fieldsHigh correlation
Dietary Fiber (% Daily Value) is highly correlated with Calories and 8 other fieldsHigh correlation
Sugars is highly correlated with Serving Size and 3 other fieldsHigh correlation
Protein is highly correlated with Calories and 13 other fieldsHigh correlation
Vitamin A (% Daily Value) is highly correlated with Calcium (% Daily Value)High correlation
Calcium (% Daily Value) is highly correlated with Calories and 4 other fieldsHigh correlation
Iron (% Daily Value) is highly correlated with Serving Size and 13 other fieldsHigh correlation
Calories is highly correlated with Calories from Fat and 10 other fieldsHigh correlation
Calories from Fat is highly correlated with Calories and 10 other fieldsHigh correlation
Total Fat is highly correlated with Calories and 10 other fieldsHigh correlation
Total Fat (% Daily Value) is highly correlated with Calories and 10 other fieldsHigh correlation
Saturated Fat is highly correlated with Calories and 7 other fieldsHigh correlation
Saturated Fat (% Daily Value) is highly correlated with Calories and 7 other fieldsHigh correlation
Trans Fat is highly correlated with Saturated Fat and 1 other fieldsHigh correlation
Cholesterol is highly correlated with Calories and 9 other fieldsHigh correlation
Cholesterol (% Daily Value) is highly correlated with Calories and 9 other fieldsHigh correlation
Sodium is highly correlated with Calories and 10 other fieldsHigh correlation
Sodium (% Daily Value) is highly correlated with Calories and 10 other fieldsHigh correlation
Carbohydrates is highly correlated with Carbohydrates (% Daily Value) and 1 other fieldsHigh correlation
Carbohydrates (% Daily Value) is highly correlated with Carbohydrates and 1 other fieldsHigh correlation
Dietary Fiber is highly correlated with Sodium and 4 other fieldsHigh correlation
Dietary Fiber (% Daily Value) is highly correlated with Sodium and 4 other fieldsHigh correlation
Sugars is highly correlated with Carbohydrates and 1 other fieldsHigh correlation
Protein is highly correlated with Calories and 10 other fieldsHigh correlation
Vitamin A (% Daily Value) is highly correlated with Calcium (% Daily Value)High correlation
Calcium (% Daily Value) is highly correlated with Vitamin A (% Daily Value)High correlation
Iron (% Daily Value) is highly correlated with Calories and 8 other fieldsHigh correlation
Total Fat (% Daily Value) is highly correlated with Calories from Fat and 17 other fieldsHigh correlation
Calories from Fat is highly correlated with Total Fat (% Daily Value) and 17 other fieldsHigh correlation
Vitamin A (% Daily Value) is highly correlated with Category and 10 other fieldsHigh correlation
Vitamin C (% Daily Value) is highly correlated with Sodium and 1 other fieldsHigh correlation
Sodium is highly correlated with Total Fat (% Daily Value) and 20 other fieldsHigh correlation
Category is highly correlated with Total Fat (% Daily Value) and 17 other fieldsHigh correlation
Protein is highly correlated with Total Fat (% Daily Value) and 20 other fieldsHigh correlation
Trans Fat is highly correlated with Total Fat (% Daily Value) and 18 other fieldsHigh correlation
Sugars is highly correlated with Vitamin A (% Daily Value) and 13 other fieldsHigh correlation
Cholesterol is highly correlated with Total Fat (% Daily Value) and 19 other fieldsHigh correlation
Dietary Fiber is highly correlated with Total Fat (% Daily Value) and 11 other fieldsHigh correlation
Saturated Fat (% Daily Value) is highly correlated with Total Fat (% Daily Value) and 19 other fieldsHigh correlation
Carbohydrates (% Daily Value) is highly correlated with Sodium and 13 other fieldsHigh correlation
Serving Size is highly correlated with Total Fat (% Daily Value) and 20 other fieldsHigh correlation
Saturated Fat is highly correlated with Total Fat (% Daily Value) and 18 other fieldsHigh correlation
Carbohydrates is highly correlated with Total Fat (% Daily Value) and 16 other fieldsHigh correlation
Calories is highly correlated with Total Fat (% Daily Value) and 19 other fieldsHigh correlation
Sodium (% Daily Value) is highly correlated with Total Fat (% Daily Value) and 20 other fieldsHigh correlation
Iron (% Daily Value) is highly correlated with Total Fat (% Daily Value) and 16 other fieldsHigh correlation
Total Fat is highly correlated with Total Fat (% Daily Value) and 17 other fieldsHigh correlation
Dietary Fiber (% Daily Value) is highly correlated with Total Fat (% Daily Value) and 16 other fieldsHigh correlation
Cholesterol (% Daily Value) is highly correlated with Total Fat (% Daily Value) and 18 other fieldsHigh correlation
Calcium (% Daily Value) is highly correlated with Total Fat (% Daily Value) and 20 other fieldsHigh correlation
Item is uniformly distributed Uniform
Item has unique values Unique
Calories has 16 (6.2%) zeros Zeros
Calories from Fat has 54 (20.8%) zeros Zeros
Total Fat has 49 (18.8%) zeros Zeros
Total Fat (% Daily Value) has 49 (18.8%) zeros Zeros
Saturated Fat has 60 (23.1%) zeros Zeros
Saturated Fat (% Daily Value) has 60 (23.1%) zeros Zeros
Cholesterol has 44 (16.9%) zeros Zeros
Cholesterol (% Daily Value) has 44 (16.9%) zeros Zeros
Sodium has 9 (3.5%) zeros Zeros
Sodium (% Daily Value) has 20 (7.7%) zeros Zeros
Carbohydrates has 16 (6.2%) zeros Zeros
Carbohydrates (% Daily Value) has 16 (6.2%) zeros Zeros
Dietary Fiber has 69 (26.5%) zeros Zeros
Dietary Fiber (% Daily Value) has 69 (26.5%) zeros Zeros
Sugars has 25 (9.6%) zeros Zeros
Protein has 27 (10.4%) zeros Zeros
Vitamin A (% Daily Value) has 62 (23.8%) zeros Zeros
Vitamin C (% Daily Value) has 208 (80.0%) zeros Zeros
Calcium (% Daily Value) has 36 (13.8%) zeros Zeros
Iron (% Daily Value) has 82 (31.5%) zeros Zeros

Reproduction

Analysis started2021-07-27 10:09:09.662345
Analysis finished2021-07-27 10:10:07.779299
Duration58.12 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Category
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
Coffee & Tea
95 
Breakfast
42 
Smoothies & Shakes
28 
Beverages
27 
Chicken & Fish
27 
Other values (4)
41 

Length

Max length18
Median length12
Mean length11.85384615
Min length6

Characters and Unicode

Total characters3082
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBreakfast
2nd rowBreakfast
3rd rowBreakfast
4th rowBreakfast
5th rowBreakfast

Common Values

ValueCountFrequency (%)
Coffee & Tea95
36.5%
Breakfast42
16.2%
Smoothies & Shakes28
 
10.8%
Beverages27
 
10.4%
Chicken & Fish27
 
10.4%
Beef & Pork15
 
5.8%
Snacks & Sides13
 
5.0%
Desserts7
 
2.7%
Salads6
 
2.3%

Length

2021-07-27T15:40:07.972430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-27T15:40:08.060368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
178
28.9%
coffee95
15.4%
tea95
15.4%
breakfast42
 
6.8%
smoothies28
 
4.5%
shakes28
 
4.5%
beverages27
 
4.4%
chicken27
 
4.4%
fish27
 
4.4%
pork15
 
2.4%
Other values (5)54
 
8.8%

Most occurring characters

ValueCountFrequency (%)
e548
17.8%
356
11.6%
a259
 
8.4%
f247
 
8.0%
s205
 
6.7%
&178
 
5.8%
o166
 
5.4%
k125
 
4.1%
C122
 
4.0%
h110
 
3.6%
Other values (16)766
24.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2110
68.5%
Uppercase Letter438
 
14.2%
Space Separator356
 
11.6%
Other Punctuation178
 
5.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e548
26.0%
a259
12.3%
f247
11.7%
s205
 
9.7%
o166
 
7.9%
k125
 
5.9%
h110
 
5.2%
i95
 
4.5%
r91
 
4.3%
t77
 
3.6%
Other values (7)187
 
8.9%
Uppercase Letter
ValueCountFrequency (%)
C122
27.9%
T95
21.7%
S88
20.1%
B84
19.2%
F27
 
6.2%
P15
 
3.4%
D7
 
1.6%
Space Separator
ValueCountFrequency (%)
356
100.0%
Other Punctuation
ValueCountFrequency (%)
&178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2548
82.7%
Common534
 
17.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e548
21.5%
a259
10.2%
f247
9.7%
s205
 
8.0%
o166
 
6.5%
k125
 
4.9%
C122
 
4.8%
h110
 
4.3%
i95
 
3.7%
T95
 
3.7%
Other values (14)576
22.6%
Common
ValueCountFrequency (%)
356
66.7%
&178
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3082
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e548
17.8%
356
11.6%
a259
 
8.4%
f247
 
8.0%
s205
 
6.7%
&178
 
5.8%
o166
 
5.4%
k125
 
4.1%
C122
 
4.0%
h110
 
3.6%
Other values (16)766
24.9%

Item
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct260
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size22.4 KiB
Egg McMuffin
 
1
Premium Southwest Salad with Crispy Chicken
 
1
Hotcakes
 
1
Hot Chocolate (Small)
 
1
Nonfat Caramel Latte (Small)
 
1
Other values (255)
255 

Length

Max length61
Median length28
Mean length28.98461538
Min length5

Characters and Unicode

Total characters7536
Distinct characters62
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique260 ?
Unique (%)100.0%

Sample

1st rowEgg McMuffin
2nd rowEgg White Delight
3rd rowSausage McMuffin
4th rowSausage McMuffin with Egg
5th rowSausage McMuffin with Egg Whites

Common Values

ValueCountFrequency (%)
Egg McMuffin1
 
0.4%
Premium Southwest Salad with Crispy Chicken1
 
0.4%
Hotcakes1
 
0.4%
Hot Chocolate (Small)1
 
0.4%
Nonfat Caramel Latte (Small)1
 
0.4%
Bacon, Egg & Cheese Bagel with Egg Whites1
 
0.4%
Strawberry Banana Smoothie (Large)1
 
0.4%
Double Quarter Pounder with Cheese1
 
0.4%
Frappé Mocha (Small)1
 
0.4%
Hash Brown1
 
0.4%
Other values (250)250
96.2%

Length

2021-07-27T15:40:08.392351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
large55
 
4.9%
with51
 
4.6%
medium48
 
4.3%
small46
 
4.1%
chicken44
 
3.9%
biscuit34
 
3.1%
iced31
 
2.8%
latte30
 
2.7%
egg30
 
2.7%
nonfat30
 
2.7%
Other values (141)715
64.2%

Most occurring characters

ValueCountFrequency (%)
854
 
11.3%
e700
 
9.3%
a589
 
7.8%
i433
 
5.7%
r349
 
4.6%
t336
 
4.5%
l315
 
4.2%
c295
 
3.9%
h267
 
3.5%
u248
 
3.3%
Other values (52)3150
41.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5167
68.6%
Uppercase Letter1079
 
14.3%
Space Separator854
 
11.3%
Open Punctuation188
 
2.5%
Close Punctuation188
 
2.5%
Other Punctuation39
 
0.5%
Decimal Number12
 
0.2%
Dash Punctuation6
 
0.1%
Final Punctuation3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e700
13.5%
a589
11.4%
i433
 
8.4%
r349
 
6.8%
t336
 
6.5%
l315
 
6.1%
c295
 
5.7%
h267
 
5.2%
u248
 
4.8%
n239
 
4.6%
Other values (15)1396
27.0%
Uppercase Letter
ValueCountFrequency (%)
C187
17.3%
S162
15.0%
M152
14.1%
B98
9.1%
L88
8.2%
F61
 
5.7%
P43
 
4.0%
N35
 
3.2%
I32
 
3.0%
E30
 
2.8%
Other values (13)191
17.7%
Decimal Number
ValueCountFrequency (%)
05
41.7%
13
25.0%
42
 
16.7%
61
 
8.3%
21
 
8.3%
Other Punctuation
ValueCountFrequency (%)
&23
59.0%
,11
28.2%
'3
 
7.7%
%2
 
5.1%
Space Separator
ValueCountFrequency (%)
854
100.0%
Open Punctuation
ValueCountFrequency (%)
(188
100.0%
Close Punctuation
ValueCountFrequency (%)
)188
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6
100.0%
Final Punctuation
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6246
82.9%
Common1290
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e700
 
11.2%
a589
 
9.4%
i433
 
6.9%
r349
 
5.6%
t336
 
5.4%
l315
 
5.0%
c295
 
4.7%
h267
 
4.3%
u248
 
4.0%
n239
 
3.8%
Other values (38)2475
39.6%
Common
ValueCountFrequency (%)
854
66.2%
(188
 
14.6%
)188
 
14.6%
&23
 
1.8%
,11
 
0.9%
-6
 
0.5%
05
 
0.4%
13
 
0.2%
'3
 
0.2%
3
 
0.2%
Other values (4)6
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII7523
99.8%
Latin 1 Sup10
 
0.1%
Punctuation3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
854
 
11.4%
e700
 
9.3%
a589
 
7.8%
i433
 
5.8%
r349
 
4.6%
t336
 
4.5%
l315
 
4.2%
c295
 
3.9%
h267
 
3.5%
u248
 
3.3%
Other values (49)3137
41.7%
Latin 1 Sup
ValueCountFrequency (%)
é9
90.0%
ñ1
 
10.0%
Punctuation
ValueCountFrequency (%)
3
100.0%

Serving Size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct78
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.93384615
Minimum1
Maximum22.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:08.536294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.495
Q16.775
median12
Q316
95-th percentile22
Maximum22.8
Range21.8
Interquartile range (IQR)9.225

Descriptive statistics

Standard deviation5.824943751
Coefficient of variation (CV)0.4881028024
Kurtosis-0.9392857679
Mean11.93384615
Median Absolute Deviation (MAD)4
Skewness0.1336506783
Sum3102.8
Variance33.92996971
MonotonicityNot monotonic
2021-07-27T15:40:08.682550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1250
19.2%
1645
17.3%
2220
 
7.7%
2016
 
6.2%
217
 
2.7%
5.77
 
2.7%
7.15
 
1.9%
15
 
1.9%
5.93
 
1.2%
53
 
1.2%
Other values (68)99
38.1%
ValueCountFrequency (%)
15
1.9%
1.21
 
0.4%
1.31
 
0.4%
21
 
0.4%
2.31
 
0.4%
2.61
 
0.4%
2.71
 
0.4%
3.11
 
0.4%
3.41
 
0.4%
3.51
 
0.4%
ValueCountFrequency (%)
22.81
 
0.4%
2220
7.7%
217
 
2.7%
2016
 
6.2%
16.91
 
0.4%
16.21
 
0.4%
1645
17.3%
15.41
 
0.4%
15.31
 
0.4%
14.91
 
0.4%

Calories
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct81
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.6522467
Minimum0
Maximum850
Zeros16
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:08.817139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1210
median335
Q3462.5
95-th percentile690.5
Maximum850
Range850
Interquartile range (IQR)252.5

Descriptive statistics

Standard deviation197.3077452
Coefficient of variation (CV)0.5691806329
Kurtosis-0.2373202232
Mean346.6522467
Median Absolute Deviation (MAD)125
Skewness0.3534392321
Sum90129.58415
Variance38930.34631
MonotonicityNot monotonic
2021-07-27T15:40:09.009060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016
 
6.2%
34010
 
3.8%
2809
 
3.5%
4308
 
3.1%
2508
 
3.1%
3307
 
2.7%
1407
 
2.7%
2607
 
2.7%
4506
 
2.3%
2706
 
2.3%
Other values (71)176
67.7%
ValueCountFrequency (%)
016
6.2%
151
 
0.4%
201
 
0.4%
451
 
0.4%
802
 
0.8%
1005
 
1.9%
1102
 
0.8%
1202
 
0.8%
1304
 
1.5%
1407
2.7%
ValueCountFrequency (%)
8502
0.8%
836.96937031
0.4%
8202
0.8%
8101
0.4%
8001
0.4%
7601
0.4%
7502
0.8%
7401
0.4%
7201
0.4%
7001
0.4%

Calories from Fat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct45
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.3706625
Minimum0
Maximum470
Zeros54
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:09.200983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120
median100
Q3200
95-th percentile290.5
Maximum470
Range470
Interquartile range (IQR)180

Descriptive statistics

Standard deviation104.9523933
Coefficient of variation (CV)0.8866419358
Kurtosis-0.03922515086
Mean118.3706625
Median Absolute Deviation (MAD)90
Skewness0.7294914644
Sum30776.37225
Variance11015.00487
MonotonicityNot monotonic
2021-07-27T15:40:09.416960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
054
20.8%
8016
 
6.2%
10011
 
4.2%
15011
 
4.2%
12010
 
3.8%
20010
 
3.8%
2109
 
3.5%
1808
 
3.1%
2808
 
3.1%
407
 
2.7%
Other values (35)116
44.6%
ValueCountFrequency (%)
054
20.8%
55
 
1.9%
105
 
1.9%
202
 
0.8%
306
 
2.3%
354
 
1.5%
407
 
2.7%
454
 
1.5%
504
 
1.5%
606
 
2.3%
ValueCountFrequency (%)
4701
0.4%
4301
0.4%
425.14705421
0.4%
4101
0.4%
3801
0.4%
3701
0.4%
3601
0.4%
3401
0.4%
3302
0.8%
3101
0.4%

Total Fat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct48
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.20769231
Minimum0
Maximum52
Zeros49
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:09.600890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.375
median11
Q322
95-th percentile33
Maximum52
Range52
Interquartile range (IQR)19.625

Descriptive statistics

Standard deviation11.69558948
Coefficient of variation (CV)0.8855134723
Kurtosis0.05068401015
Mean13.20769231
Median Absolute Deviation (MAD)10
Skewness0.7514739729
Sum3434
Variance136.7868132
MonotonicityNot monotonic
2021-07-27T15:40:09.816731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
049
 
18.8%
914
 
5.4%
2312
 
4.6%
1111
 
4.2%
1610
 
3.8%
208
 
3.1%
3.58
 
3.1%
88
 
3.1%
0.58
 
3.1%
197
 
2.7%
Other values (38)125
48.1%
ValueCountFrequency (%)
049
18.8%
0.58
 
3.1%
16
 
2.3%
1.51
 
0.4%
21
 
0.4%
2.51
 
0.4%
3.58
 
3.1%
43
 
1.2%
4.56
 
2.3%
55
 
1.9%
ValueCountFrequency (%)
521
0.4%
501
0.4%
481
0.4%
461
0.4%
431
0.4%
411
0.4%
401
0.4%
381
0.4%
372
0.8%
351
0.4%

Total Fat (% Daily Value)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct59
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.33461538
Minimum0
Maximum80
Zeros49
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:09.997109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.75
median17
Q333.25
95-th percentile50.05
Maximum80
Range80
Interquartile range (IQR)29.5

Descriptive statistics

Standard deviation17.98883056
Coefficient of variation (CV)0.8846408069
Kurtosis0.02735148697
Mean20.33461538
Median Absolute Deviation (MAD)15
Skewness0.7473830035
Sum5287
Variance323.5980249
MonotonicityNot monotonic
2021-07-27T15:40:10.245407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
049
 
18.8%
1311
 
4.2%
111
 
4.2%
3510
 
3.8%
259
 
3.5%
179
 
3.5%
218
 
3.1%
147
 
2.7%
116
 
2.3%
86
 
2.3%
Other values (49)134
51.5%
ValueCountFrequency (%)
049
18.8%
111
 
4.2%
24
 
1.5%
31
 
0.4%
41
 
0.4%
55
 
1.9%
66
 
2.3%
76
 
2.3%
86
 
2.3%
92
 
0.8%
ValueCountFrequency (%)
801
0.4%
771
0.4%
731
0.4%
701
0.4%
661
0.4%
631
0.4%
621
0.4%
591
0.4%
572
0.8%
531
0.4%

Saturated Fat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.007692308
Minimum0
Maximum20
Zeros60
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:10.554408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q310
95-th percentile16
Maximum20
Range20
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.321873206
Coefficient of variation (CV)0.8858431713
Kurtosis-0.4264662884
Mean6.007692308
Median Absolute Deviation (MAD)4.25
Skewness0.6636847965
Sum1562
Variance28.32233442
MonotonicityNot monotonic
2021-07-27T15:40:10.802238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
060
23.1%
818
 
6.9%
1016
 
6.2%
315
 
5.8%
615
 
5.8%
713
 
5.0%
1212
 
4.6%
511
 
4.2%
4.511
 
4.2%
911
 
4.2%
Other values (16)78
30.0%
ValueCountFrequency (%)
060
23.1%
0.53
 
1.2%
13
 
1.2%
1.55
 
1.9%
210
 
3.8%
2.56
 
2.3%
315
 
5.8%
3.59
 
3.5%
43
 
1.2%
4.511
 
4.2%
ValueCountFrequency (%)
204
 
1.5%
192
 
0.8%
181
 
0.4%
174
 
1.5%
163
 
1.2%
158
3.1%
147
2.7%
137
2.7%
1212
4.6%
113
 
1.2%

Saturated Fat (% Daily Value)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct80
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.9204184
Minimum0
Maximum102
Zeros60
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:11.016149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.75
median24
Q348
95-th percentile76.31860378
Maximum102
Range102
Interquartile range (IQR)43.25

Descriptive statistics

Standard deviation26.59174463
Coefficient of variation (CV)0.88874909
Kurtosis-0.3610946426
Mean29.9204184
Median Absolute Deviation (MAD)21
Skewness0.6863595939
Sum7779.308784
Variance707.1208822
MonotonicityNot monotonic
2021-07-27T15:40:11.223569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
060
23.1%
1510
 
3.8%
2210
 
3.8%
117
 
2.7%
297
 
2.7%
246
 
2.3%
396
 
2.3%
305
 
1.9%
405
 
1.9%
185
 
1.9%
Other values (70)139
53.5%
ValueCountFrequency (%)
060
23.1%
33
 
1.2%
42
 
0.8%
51
 
0.4%
61
 
0.4%
84
 
1.5%
93
 
1.2%
102
 
0.8%
117
 
2.7%
122
 
0.8%
ValueCountFrequency (%)
1021
0.4%
1012
0.8%
1001
0.4%
962
0.8%
901
0.4%
872
0.8%
852
0.8%
811
0.4%
781
0.4%
76.230109241
0.4%

Trans Fat
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
0.0
204 
1.0
30 
0.5
 
17
1.5
 
8
2.5
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters780
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0204
78.5%
1.030
 
11.5%
0.517
 
6.5%
1.58
 
3.1%
2.51
 
0.4%

Length

2021-07-27T15:40:11.620848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-27T15:40:11.771042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0204
78.5%
1.030
 
11.5%
0.517
 
6.5%
1.58
 
3.1%
2.51
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0455
58.3%
.260
33.3%
138
 
4.9%
526
 
3.3%
21
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number520
66.7%
Other Punctuation260
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0455
87.5%
138
 
7.3%
526
 
5.0%
21
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.260
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common780
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0455
58.3%
.260
33.3%
138
 
4.9%
526
 
3.3%
21
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII780
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0455
58.3%
.260
33.3%
138
 
4.9%
526
 
3.3%
21
 
0.1%

Cholesterol
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.34615385
Minimum0
Maximum135
Zeros44
Zeros (%)16.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:11.938247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median30
Q350
95-th percentile90
Maximum135
Range135
Interquartile range (IQR)45

Descriptive statistics

Standard deviation29.87353306
Coefficient of variation (CV)0.8697781182
Kurtosis-0.006523064821
Mean34.34615385
Median Absolute Deviation (MAD)20
Skewness0.8151427154
Sum8930
Variance892.4279774
MonotonicityNot monotonic
2021-07-27T15:40:12.101660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
044
16.9%
3032
12.3%
524
 
9.2%
3521
 
8.1%
2517
 
6.5%
4014
 
5.4%
5012
 
4.6%
1512
 
4.6%
109
 
3.5%
208
 
3.1%
Other values (14)67
25.8%
ValueCountFrequency (%)
044
16.9%
524
9.2%
109
 
3.5%
1512
 
4.6%
208
 
3.1%
2517
 
6.5%
3032
12.3%
3521
8.1%
4014
 
5.4%
457
 
2.7%
ValueCountFrequency (%)
1351
 
0.4%
1152
 
0.8%
1101
 
0.4%
1053
 
1.2%
954
1.5%
908
3.1%
855
1.9%
807
2.7%
757
2.7%
704
1.5%

Cholesterol (% Daily Value)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct36
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.51923077
Minimum0
Maximum44
Zeros44
Zeros (%)16.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:12.291728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median10
Q317
95-th percentile30
Maximum44
Range44
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.9226553
Coefficient of variation (CV)0.8613991246
Kurtosis-0.07419926711
Mean11.51923077
Median Absolute Deviation (MAD)7
Skewness0.7840449713
Sum2995
Variance98.45908821
MonotonicityNot monotonic
2021-07-27T15:40:12.451704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
044
16.9%
1025
 
9.6%
218
 
6.9%
1116
 
6.2%
1413
 
5.0%
913
 
5.0%
1211
 
4.2%
179
 
3.5%
39
 
3.5%
68
 
3.1%
Other values (26)94
36.2%
ValueCountFrequency (%)
044
16.9%
16
 
2.3%
218
6.9%
39
 
3.5%
56
 
2.3%
68
 
3.1%
76
 
2.3%
85
 
1.9%
913
 
5.0%
1025
9.6%
ValueCountFrequency (%)
441
 
0.4%
382
 
0.8%
371
 
0.4%
352
 
0.8%
341
 
0.4%
323
1.2%
312
 
0.8%
305
1.9%
295
1.9%
282
 
0.8%

Sodium
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct112
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean444.2675234
Minimum0
Maximum1800
Zeros9
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:12.628899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q1107.5
median190
Q3812.5
95-th percentile1422.5
Maximum1800
Range1800
Interquartile range (IQR)705

Descriptive statistics

Standard deviation489.8050446
Coefficient of variation (CV)1.102500225
Kurtosis-0.2927626826
Mean444.2675234
Median Absolute Deviation (MAD)140
Skewness1.074830687
Sum115509.5561
Variance239908.9817
MonotonicityNot monotonic
2021-07-27T15:40:12.852914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19013
 
5.0%
18011
 
4.2%
09
 
3.5%
1508
 
3.1%
1408
 
3.1%
107
 
2.7%
1156
 
2.3%
1356
 
2.3%
1606
 
2.3%
1706
 
2.3%
Other values (102)180
69.2%
ValueCountFrequency (%)
09
3.5%
55
1.9%
107
2.7%
153
 
1.2%
15.442173411
 
0.4%
201
 
0.4%
251
 
0.4%
302
 
0.8%
354
1.5%
403
 
1.2%
ValueCountFrequency (%)
18001
 
0.4%
17001
 
0.4%
16801
 
0.4%
15901
 
0.4%
15602
0.8%
1516.4935241
 
0.4%
15101
 
0.4%
14803
1.2%
14702
0.8%
14201
 
0.4%

Sodium (% Daily Value)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct60
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.83076923
Minimum0
Maximum75
Zeros20
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:13.017386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.75
median8
Q334
95-th percentile61
Maximum75
Range75
Interquartile range (IQR)29.25

Descriptive statistics

Standard deviation20.80990945
Coefficient of variation (CV)1.105101401
Kurtosis-0.3096956942
Mean18.83076923
Median Absolute Deviation (MAD)6
Skewness1.071129835
Sum4896
Variance433.0523315
MonotonicityNot monotonic
2021-07-27T15:40:13.185363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
623
 
8.8%
821
 
8.1%
020
 
7.7%
717
 
6.5%
516
 
6.2%
215
 
5.8%
313
 
5.0%
112
 
4.6%
99
 
3.5%
107
 
2.7%
Other values (50)107
41.2%
ValueCountFrequency (%)
020
7.7%
112
4.6%
215
5.8%
313
5.0%
45
 
1.9%
516
6.2%
623
8.8%
717
6.5%
821
8.1%
99
 
3.5%
ValueCountFrequency (%)
751
 
0.4%
721
 
0.4%
711
 
0.4%
701
 
0.4%
661
 
0.4%
652
0.8%
641
 
0.4%
631
 
0.4%
623
1.2%
612
0.8%

Carbohydrates
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct72
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.32307692
Minimum0
Maximum98
Zeros16
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:13.330076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median43
Q355
95-th percentile76
Maximum98
Range98
Interquartile range (IQR)25

Descriptive statistics

Standard deviation20.48512612
Coefficient of variation (CV)0.4840178835
Kurtosis0.1623505576
Mean42.32307692
Median Absolute Deviation (MAD)13
Skewness-0.03675645808
Sum11004
Variance419.640392
MonotonicityNot monotonic
2021-07-27T15:40:13.537694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4323
 
8.8%
016
 
6.2%
509
 
3.5%
308
 
3.1%
417
 
2.7%
427
 
2.7%
406
 
2.3%
346
 
2.3%
386
 
2.3%
476
 
2.3%
Other values (62)166
63.8%
ValueCountFrequency (%)
016
6.2%
42
 
0.8%
71
 
0.4%
81
 
0.4%
91
 
0.4%
101
 
0.4%
123
 
1.2%
154
 
1.5%
183
 
1.2%
191
 
0.4%
ValueCountFrequency (%)
981
 
0.4%
962
0.8%
912
0.8%
901
 
0.4%
861
 
0.4%
802
0.8%
792
0.8%
781
 
0.4%
762
0.8%
743
1.2%

Carbohydrates (% Daily Value)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct33
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.16923077
Minimum0
Maximum35
Zeros16
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:13.736066image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median14
Q318
95-th percentile25.05
Maximum35
Range35
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.965040625
Coefficient of variation (CV)0.4915609562
Kurtosis0.2570147287
Mean14.16923077
Median Absolute Deviation (MAD)4
Skewness0.03896630381
Sum3684
Variance48.51179091
MonotonicityNot monotonic
2021-07-27T15:40:13.923397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1436
 
13.8%
016
 
6.2%
1316
 
6.2%
1015
 
5.8%
1615
 
5.8%
1714
 
5.4%
1513
 
5.0%
2012
 
4.6%
1811
 
4.2%
1911
 
4.2%
Other values (23)101
38.8%
ValueCountFrequency (%)
016
6.2%
12
 
0.8%
21
 
0.4%
33
 
1.2%
43
 
1.2%
54
 
1.5%
64
 
1.5%
78
3.1%
87
2.7%
98
3.1%
ValueCountFrequency (%)
351
 
0.4%
331
 
0.4%
322
 
0.8%
303
1.2%
291
 
0.4%
272
 
0.8%
263
1.2%
255
1.9%
245
1.9%
232
 
0.8%

Dietary Fiber
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.630769231
Minimum0
Maximum7
Zeros69
Zeros (%)26.5%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:14.078507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.567717085
Coefficient of variation (CV)0.9613359482
Kurtosis1.323632515
Mean1.630769231
Median Absolute Deviation (MAD)1
Skewness1.173624825
Sum424
Variance2.457736858
MonotonicityNot monotonic
2021-07-27T15:40:14.222032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
180
30.8%
069
26.5%
244
16.9%
338
14.6%
415
 
5.8%
56
 
2.3%
64
 
1.5%
74
 
1.5%
ValueCountFrequency (%)
069
26.5%
180
30.8%
244
16.9%
338
14.6%
415
 
5.8%
56
 
2.3%
64
 
1.5%
74
 
1.5%
ValueCountFrequency (%)
74
 
1.5%
64
 
1.5%
56
 
2.3%
415
 
5.8%
338
14.6%
244
16.9%
180
30.8%
069
26.5%

Dietary Fiber (% Daily Value)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct23
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.176923077
Minimum0
Maximum24
Zeros69
Zeros (%)26.5%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:14.382600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q310
95-th percentile17
Maximum24
Range24
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.707091551
Coefficient of variation (CV)0.923937611
Kurtosis0.3338223718
Mean6.176923077
Median Absolute Deviation (MAD)5
Skewness0.9167154036
Sum1606
Variance32.57089397
MonotonicityNot monotonic
2021-07-27T15:40:14.521051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
069
26.5%
525
 
9.6%
623
 
8.8%
422
 
8.5%
319
 
7.3%
714
 
5.4%
1312
 
4.6%
1210
 
3.8%
810
 
3.8%
109
 
3.5%
Other values (13)47
18.1%
ValueCountFrequency (%)
069
26.5%
25
 
1.9%
319
 
7.3%
422
 
8.5%
525
 
9.6%
623
 
8.8%
714
 
5.4%
810
 
3.8%
95
 
1.9%
109
 
3.5%
ValueCountFrequency (%)
241
 
0.4%
233
1.2%
222
 
0.8%
201
 
0.4%
193
1.2%
181
 
0.4%
177
2.7%
161
 
0.4%
154
1.5%
146
2.3%

Sugars
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct79
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.81538462
Minimum0
Maximum103
Zeros25
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:14.695452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.75
median17
Q346
95-th percentile77.1
Maximum103
Range103
Interquartile range (IQR)40.25

Descriptive statistics

Standard deviation26.28762341
Coefficient of variation (CV)0.9450749567
Kurtosis-0.2251599355
Mean27.81538462
Median Absolute Deviation (MAD)15
Skewness0.8494679139
Sum7232
Variance691.0391446
MonotonicityNot monotonic
2021-07-27T15:40:14.849859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
025
 
9.6%
317
 
6.5%
211
 
4.2%
710
 
3.8%
68
 
3.1%
177
 
2.7%
157
 
2.7%
127
 
2.7%
596
 
2.3%
106
 
2.3%
Other values (69)156
60.0%
ValueCountFrequency (%)
025
9.6%
12
 
0.8%
211
4.2%
317
6.5%
46
 
2.3%
54
 
1.5%
68
 
3.1%
710
 
3.8%
84
 
1.5%
94
 
1.5%
ValueCountFrequency (%)
1031
0.4%
1011
0.4%
1001
0.4%
991
0.4%
971
0.4%
931
0.4%
891
0.4%
882
0.8%
851
0.4%
812
0.8%

Protein
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct39
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.78846154
Minimum0
Maximum40
Zeros27
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:15.006830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median12
Q318
95-th percentile33.1
Maximum40
Range40
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.05263472
Coefficient of variation (CV)0.7860706848
Kurtosis-0.1113348359
Mean12.78846154
Median Absolute Deviation (MAD)7
Skewness0.6848186342
Sum3325
Variance101.0554648
MonotonicityNot monotonic
2021-07-27T15:40:15.745437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
027
 
10.4%
118
 
6.9%
1217
 
6.5%
914
 
5.4%
213
 
5.0%
1412
 
4.6%
1512
 
4.6%
1112
 
4.6%
1011
 
4.2%
1610
 
3.8%
Other values (29)114
43.8%
ValueCountFrequency (%)
027
10.4%
118
6.9%
213
5.0%
35
 
1.9%
47
 
2.7%
53
 
1.2%
64
 
1.5%
73
 
1.2%
89
 
3.5%
914
5.4%
ValueCountFrequency (%)
402
 
0.8%
391
 
0.4%
372
 
0.8%
366
2.3%
352
 
0.8%
331
 
0.4%
322
 
0.8%
311
 
0.4%
304
1.5%
292
 
0.8%

Vitamin A (% Daily Value)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.201049498
Minimum0
Maximum30
Zeros62
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:15.892132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q315
95-th percentile20
Maximum30
Range30
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.310115402
Coefficient of variation (CV)0.8913634046
Kurtosis0.2011209244
Mean8.201049498
Median Absolute Deviation (MAD)6.5
Skewness0.8038633855
Sum2132.27287
Variance53.43778719
MonotonicityNot monotonic
2021-07-27T15:40:16.013294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
062
23.8%
1540
15.4%
1040
15.4%
830
11.5%
425
9.6%
218
 
6.9%
2017
 
6.5%
615
 
5.8%
256
 
2.3%
305
 
1.9%
Other values (2)2
 
0.8%
ValueCountFrequency (%)
062
23.8%
218
 
6.9%
425
9.6%
615
 
5.8%
830
11.5%
1040
15.4%
12.881099841
 
0.4%
13.391769711
 
0.4%
1540
15.4%
2017
 
6.5%
ValueCountFrequency (%)
305
 
1.9%
256
 
2.3%
2017
6.5%
1540
15.4%
13.391769711
 
0.4%
12.881099841
 
0.4%
1040
15.4%
830
11.5%
615
 
5.8%
425
9.6%

Vitamin C (% Daily Value)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8230769231
Minimum0
Maximum10
Zeros208
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:16.148637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.112530677
Coefficient of variation (CV)2.566626057
Kurtosis9.03260103
Mean0.8230769231
Median Absolute Deviation (MAD)0
Skewness3.077550575
Sum214
Variance4.462785863
MonotonicityNot monotonic
2021-07-27T15:40:16.300682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0208
80.0%
231
 
11.9%
89
 
3.5%
46
 
2.3%
105
 
1.9%
61
 
0.4%
ValueCountFrequency (%)
0208
80.0%
231
 
11.9%
46
 
2.3%
61
 
0.4%
89
 
3.5%
105
 
1.9%
ValueCountFrequency (%)
105
 
1.9%
89
 
3.5%
61
 
0.4%
46
 
2.3%
231
 
11.9%
0208
80.0%

Calcium (% Daily Value)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.58846154
Minimum0
Maximum60
Zeros36
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:16.460109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median20
Q330
95-th percentile50
Maximum60
Range60
Interquartile range (IQR)24

Descriptive statistics

Standard deviation16.46136994
Coefficient of variation (CV)0.7995434681
Kurtosis-0.6862706229
Mean20.58846154
Median Absolute Deviation (MAD)12
Skewness0.5211158977
Sum5353
Variance270.9767003
MonotonicityNot monotonic
2021-07-27T15:40:16.608617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
036
13.8%
3033
12.7%
1525
9.6%
2022
8.5%
2522
8.5%
5020
7.7%
1018
6.9%
4017
6.5%
817
6.5%
414
 
5.4%
Other values (5)36
13.8%
ValueCountFrequency (%)
036
13.8%
210
 
3.8%
414
 
5.4%
66
 
2.3%
817
6.5%
1018
6.9%
1525
9.6%
2022
8.5%
2522
8.5%
3033
12.7%
ValueCountFrequency (%)
606
 
2.3%
5020
7.7%
454
 
1.5%
4017
6.5%
3510
 
3.8%
3033
12.7%
2522
8.5%
2022
8.5%
1525
9.6%
1018
6.9%

Iron (% Daily Value)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.493030939
Minimum0
Maximum35
Zeros82
Zeros (%)31.5%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2021-07-27T15:40:16.794071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q315
95-th percentile25
Maximum35
Range35
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.251859566
Coefficient of variation (CV)1.101271252
Kurtosis0.1801203941
Mean7.493030939
Median Absolute Deviation (MAD)4
Skewness1.026626385
Sum1948.188044
Variance68.0931863
MonotonicityNot monotonic
2021-07-27T15:40:16.963444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
082
31.5%
233
12.7%
1528
 
10.8%
2025
 
9.6%
421
 
8.1%
1019
 
7.3%
616
 
6.2%
814
 
5.4%
257
 
2.7%
306
 
2.3%
Other values (9)9
 
3.5%
ValueCountFrequency (%)
082
31.5%
233
12.7%
421
 
8.1%
616
 
6.2%
7.2999372091
 
0.4%
7.5233685811
 
0.4%
814
 
5.4%
8.511265691
 
0.4%
1019
 
7.3%
10.924145461
 
0.4%
ValueCountFrequency (%)
351
 
0.4%
306
 
2.3%
257
 
2.7%
2025
9.6%
15.794996311
 
0.4%
15.353953351
 
0.4%
1528
10.8%
12.532381951
 
0.4%
12.247995691
 
0.4%
10.924145461
 
0.4%

Interactions

2021-07-27T15:39:11.050150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:11.170156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:11.330601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:11.491795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:11.657220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:11.826483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:11.954433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:12.796417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:12.901664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:12.989634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:13.093591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:13.216249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:13.312243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:13.425678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:13.521640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:13.617596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:13.713561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:13.817517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:13.921524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:14.025473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:14.132002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:14.237294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:14.325261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:14.421219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:14.533677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:14.629689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:14.743007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:14.846964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:14.942923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:15.049064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:15.146323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:15.250355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:15.354239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:15.444508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:15.557751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:15.661785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:15.757672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:15.861678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:15.973582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:16.069545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:16.165505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:16.277516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:16.421537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:16.557345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:16.701285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:16.839803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:17.153052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:17.321049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:17.488917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:17.616863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:17.736816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:17.896751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:18.045550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:18.202395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:18.339736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:18.443702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:18.547651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:18.651653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:18.771563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:18.931555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:19.091429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:19.195386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:19.299342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:19.411295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:19.517597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:19.645591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:19.774945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:19.886899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:20.001745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:20.123041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:20.227007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-07-27T15:39:20.330965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-27T15:40:17.687433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
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Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
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Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

CategoryItemServing SizeCaloriesCalories from FatTotal FatTotal Fat (% Daily Value)Saturated FatSaturated Fat (% Daily Value)Trans FatCholesterolCholesterol (% Daily Value)SodiumSodium (% Daily Value)CarbohydratesCarbohydrates (% Daily Value)Dietary FiberDietary Fiber (% Daily Value)SugarsProteinVitamin A (% Daily Value)Vitamin C (% Daily Value)Calcium (% Daily Value)Iron (% Daily Value)
0BreakfastEgg McMuffin4.8300.000000120.013.020.05.025.00.030.010.0750.031.031.010.0417.03.017.010.00.025.015.0
1BreakfastEgg White Delight4.8250.00000070.08.012.03.015.00.025.08.0770.032.030.010.0417.03.018.06.00.025.08.0
2BreakfastSausage McMuffin3.9370.000000200.023.035.08.042.00.045.015.0780.033.029.010.0417.02.014.08.00.025.010.0
3BreakfastSausage McMuffin with Egg5.7450.000000250.028.043.010.052.00.030.010.0860.036.030.010.0417.02.021.015.00.030.015.0
4BreakfastSausage McMuffin with Egg Whites5.7400.000000210.023.035.08.042.00.050.016.0880.037.030.010.0417.02.021.06.00.025.010.0
5BreakfastSteak & Egg McMuffin6.5430.000000210.023.036.09.046.01.030.010.0960.040.031.010.0418.03.026.015.02.030.020.0
6BreakfastBacon, Egg & Cheese Biscuit (Regular Biscuit)5.3460.000000230.026.040.013.065.00.030.010.01300.054.038.013.027.03.019.010.08.015.015.0
7BreakfastBacon, Egg & Cheese Biscuit (Large Biscuit)5.8520.000000270.030.047.014.068.00.030.010.01410.059.043.014.0312.04.019.015.08.020.020.0
8BreakfastBacon, Egg & Cheese Biscuit with Egg Whites (Regular Biscuit)5.4370.283909180.020.032.011.056.00.035.011.01300.054.036.012.027.03.020.02.08.015.010.0
9BreakfastBacon, Egg & Cheese Biscuit with Egg Whites (Large Biscuit)5.9470.000000220.025.038.012.059.00.035.011.01420.059.042.014.0312.04.020.06.08.015.015.0

Last rows

CategoryItemServing SizeCaloriesCalories from FatTotal FatTotal Fat (% Daily Value)Saturated FatSaturated Fat (% Daily Value)Trans FatCholesterolCholesterol (% Daily Value)SodiumSodium (% Daily Value)CarbohydratesCarbohydrates (% Daily Value)Dietary FiberDietary Fiber (% Daily Value)SugarsProteinVitamin A (% Daily Value)Vitamin C (% Daily Value)Calcium (% Daily Value)Iron (% Daily Value)
250Smoothies & ShakesShamrock Shake (Medium)16.0660.0170.019.029.012.061.01.075.024.0210.09.043.014.000.093.014.025.00.050.00.0
251Smoothies & ShakesShamrock Shake (Large)22.0820.0210.023.035.015.073.01.090.029.0260.011.043.014.000.017.018.030.00.060.00.0
252Smoothies & ShakesMcFlurry with M&M’s Candies (Small)10.9650.0210.023.035.014.072.00.550.017.0180.07.096.032.016.089.013.015.00.045.08.0
253Smoothies & ShakesMcFlurry with M&M’s Candies (Medium)16.2335.0290.033.050.020.0102.01.075.025.0260.011.043.014.027.017.020.025.00.020.010.0
254Smoothies & ShakesMcFlurry with M&M’s Candies (Snack)7.3430.0140.015.024.010.048.00.035.011.0120.05.064.021.014.059.09.010.00.030.04.0
255Smoothies & ShakesMcFlurry with Oreo Cookies (Small)10.1510.0150.017.026.09.044.00.545.014.0280.012.080.027.014.064.012.015.00.040.08.0
256Smoothies & ShakesMcFlurry with Oreo Cookies (Medium)13.4690.0200.023.035.012.058.01.055.019.0380.016.043.035.015.085.015.020.00.050.010.0
257Smoothies & ShakesMcFlurry with Oreo Cookies (Snack)6.7340.0100.011.017.06.029.00.030.09.0190.08.053.018.012.043.08.010.00.025.06.0
258Smoothies & ShakesMcFlurry with Reese's Peanut Butter Cups (Medium)14.2810.0290.032.050.015.076.01.060.020.0400.017.043.014.029.0103.021.020.00.060.06.0
259Smoothies & ShakesMcFlurry with Reese's Peanut Butter Cups (Snack)7.1410.0150.016.025.08.038.00.030.010.0200.08.057.019.015.051.010.010.00.030.04.0